diff --git a/comfy/controlnet.py b/comfy/controlnet.py index f08ff4b36..0b5e30f52 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -310,11 +310,13 @@ class ControlLoraOps: self.bias = None def forward(self, input): - weight, bias = comfy.ops.cast_bias_weight(self, input) + weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True) if self.up is not None: - return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) + x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) else: - return torch.nn.functional.linear(input, weight, bias) + x = torch.nn.functional.linear(input, weight, bias) + comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream) + return x class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__( @@ -350,12 +352,13 @@ class ControlLoraOps: def forward(self, input): - weight, bias = comfy.ops.cast_bias_weight(self, input) + weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True) if self.up is not None: - return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) + x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) else: - return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) - + x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) + comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream) + return x class ControlLora(ControlNet): def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options diff --git a/comfy/model_management.py b/comfy/model_management.py index d0e2a221b..d00cb1eab 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1014,6 +1014,16 @@ if args.async_offload: NUM_STREAMS = 2 logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) +def current_stream(device): + if device is None: + return None + if is_device_cuda(device): + return torch.cuda.current_stream() + elif is_device_xpu(device): + return torch.xpu.current_stream() + else: + return None + stream_counters = {} def get_offload_stream(device): stream_counter = stream_counters.get(device, 0) @@ -1022,21 +1032,17 @@ def get_offload_stream(device): if device in STREAMS: ss = STREAMS[device] - s = ss[stream_counter] + #Sync the oldest stream in the queue with the current + ss[stream_counter].wait_stream(current_stream(device)) stream_counter = (stream_counter + 1) % len(ss) - if is_device_cuda(device): - ss[stream_counter].wait_stream(torch.cuda.current_stream()) - elif is_device_xpu(device): - ss[stream_counter].wait_stream(torch.xpu.current_stream()) stream_counters[device] = stream_counter - return s + return ss[stream_counter] elif is_device_cuda(device): ss = [] for k in range(NUM_STREAMS): ss.append(torch.cuda.Stream(device=device, priority=0)) STREAMS[device] = ss s = ss[stream_counter] - stream_counter = (stream_counter + 1) % len(ss) stream_counters[device] = stream_counter return s elif is_device_xpu(device): @@ -1045,18 +1051,14 @@ def get_offload_stream(device): ss.append(torch.xpu.Stream(device=device, priority=0)) STREAMS[device] = ss s = ss[stream_counter] - stream_counter = (stream_counter + 1) % len(ss) stream_counters[device] = stream_counter return s return None def sync_stream(device, stream): - if stream is None: + if stream is None or current_stream(device) is None: return - if is_device_cuda(device): - torch.cuda.current_stream().wait_stream(stream) - elif is_device_xpu(device): - torch.xpu.current_stream().wait_stream(stream) + current_stream(device).wait_stream(stream) def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None): if device is None or weight.device == device: diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index aec73349c..ed3f3f5cb 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -655,9 +655,11 @@ class ModelPatcher: mem_counter = 0 patch_counter = 0 lowvram_counter = 0 + lowvram_mem_counter = 0 loading = self._load_list() load_completely = [] + offloaded = [] loading.sort(reverse=True) for x in loading: n = x[1] @@ -674,6 +676,7 @@ class ModelPatcher: if mem_counter + module_mem >= lowvram_model_memory: lowvram_weight = True lowvram_counter += 1 + lowvram_mem_counter += module_mem if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed continue @@ -699,8 +702,7 @@ class ModelPatcher: patch_counter += 1 cast_weight = True - for param in params: - self.pin_weight_to_device("{}.{}".format(n, param)) + offloaded.append((module_mem, n, m, params)) else: if hasattr(m, "comfy_cast_weights"): wipe_lowvram_weight(m) @@ -741,11 +743,17 @@ class ModelPatcher: for x in load_completely: x[2].to(device_to) + for x in offloaded: + n = x[1] + params = x[3] + for param in params: + self.pin_weight_to_device("{}.{}".format(n, param)) + if lowvram_counter > 0: - logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) + logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter)) self.model.model_lowvram = True else: - logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) + logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) self.model.model_lowvram = False if full_load: self.model.to(device_to) @@ -1283,5 +1291,6 @@ class ModelPatcher: self.clear_cached_hook_weights() def __del__(self): + self.unpin_all_weights() self.detach(unpatch_all=False) diff --git a/comfy/ops.py b/comfy/ops.py index b7aea8555..6f321b55d 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -70,8 +70,12 @@ cast_to = comfy.model_management.cast_to #TODO: remove once no more references def cast_to_input(weight, input, non_blocking=False, copy=True): return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) + @torch.compiler.disable() -def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): +def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False): + # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass + # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This + # will add async-offload support to your cast and improve performance. if input is not None: if dtype is None: dtype = input.dtype @@ -80,7 +84,11 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if device is None: device = input.device - offload_stream = comfy.model_management.get_offload_stream(device) + if offloadable: + offload_stream = comfy.model_management.get_offload_stream(device) + else: + offload_stream = None + if offload_stream is not None: wf_context = offload_stream else: @@ -105,7 +113,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): weight = f(weight) comfy.model_management.sync_stream(device, offload_stream) - return weight, bias + if offloadable: + return weight, bias, offload_stream + else: + #Legacy function signature + return weight, bias + + +def uncast_bias_weight(s, weight, bias, offload_stream): + if offload_stream is None: + return + if weight is not None: + device = weight.device + else: + if bias is None: + return + device = bias.device + offload_stream.wait_stream(comfy.model_management.current_stream(device)) + class CastWeightBiasOp: comfy_cast_weights = False @@ -118,8 +143,10 @@ class disable_weight_init: return None def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = torch.nn.functional.linear(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -133,8 +160,10 @@ class disable_weight_init: return None def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = self._conv_forward(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -148,8 +177,10 @@ class disable_weight_init: return None def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = self._conv_forward(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -172,8 +203,10 @@ class disable_weight_init: return super()._conv_forward(input, weight, bias, *args, **kwargs) def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = self._conv_forward(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -187,8 +220,10 @@ class disable_weight_init: return None def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -203,11 +238,14 @@ class disable_weight_init: def forward_comfy_cast_weights(self, input): if self.weight is not None: - weight, bias = cast_bias_weight(self, input) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) else: weight = None bias = None - return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + offload_stream = None + x = torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -223,11 +261,15 @@ class disable_weight_init: def forward_comfy_cast_weights(self, input): if self.weight is not None: - weight, bias = cast_bias_weight(self, input) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) else: weight = None - return comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated - # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) + bias = None + offload_stream = None + x = comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated + # x = torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -246,10 +288,12 @@ class disable_weight_init: input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose2d( + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = torch.nn.functional.conv_transpose2d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -268,10 +312,12 @@ class disable_weight_init: input, output_size, self.stride, self.padding, self.kernel_size, num_spatial_dims, self.dilation) - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose1d( + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = torch.nn.functional.conv_transpose1d( input, weight, bias, self.stride, self.padding, output_padding, self.groups, self.dilation) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, *args, **kwargs): run_every_op() @@ -289,8 +335,11 @@ class disable_weight_init: output_dtype = out_dtype if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: out_dtype = None - weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) - return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) + weight, bias, offload_stream = cast_bias_weight(self, device=input.device, dtype=out_dtype, offloadable=True) + x = torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) + uncast_bias_weight(self, weight, bias, offload_stream) + return x + def forward(self, *args, **kwargs): run_every_op() @@ -361,7 +410,7 @@ def fp8_linear(self, input): input_dtype = input.dtype if len(input.shape) == 3: - w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype) + w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True) scale_weight = self.scale_weight scale_input = self.scale_input @@ -372,16 +421,22 @@ def fp8_linear(self, input): if scale_input is None: scale_input = torch.ones((), device=input.device, dtype=torch.float32) + input = torch.clamp(input, min=-448, max=448, out=input) + input = input.reshape(-1, input_shape[2]).to(dtype).contiguous() + layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype} + quantized_input = QuantizedTensor(input.reshape(-1, input_shape[2]).to(dtype).contiguous(), TensorCoreFP8Layout, layout_params_weight) else: scale_input = scale_input.to(input.device) + quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype) # Wrap weight in QuantizedTensor - this enables unified dispatch # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py! layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype} quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight) - quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype) o = torch.nn.functional.linear(quantized_input, quantized_weight, bias) + uncast_bias_weight(self, w, bias, offload_stream) + if tensor_2d: return o.reshape(input_shape[0], -1) return o.reshape((-1, input_shape[1], self.weight.shape[0])) @@ -404,8 +459,10 @@ class fp8_ops(manual_cast): except Exception as e: logging.info("Exception during fp8 op: {}".format(e)) - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = torch.nn.functional.linear(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None): logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input)) @@ -433,12 +490,14 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None if out is not None: return out - weight, bias = cast_bias_weight(self, input) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) if weight.numel() < input.numel(): #TODO: optimize - return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias) + x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias) else: - return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias) + x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def convert_weight(self, weight, inplace=False, **kwargs): if inplace: @@ -577,8 +636,10 @@ class MixedPrecisionOps(disable_weight_init): return torch.nn.functional.linear(input, weight, bias) def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._forward(input, weight, bias) + weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True) + x = self._forward(input, weight, bias) + uncast_bias_weight(self, weight, bias, offload_stream) + return x def forward(self, input, *args, **kwargs): run_every_op() diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py index b14e03084..c822fe53c 100644 --- a/comfy/quant_ops.py +++ b/comfy/quant_ops.py @@ -357,9 +357,10 @@ class TensorCoreFP8Layout(QuantizedLayout): scale = torch.tensor(scale) scale = scale.to(device=tensor.device, dtype=torch.float32) - lp_amax = torch.finfo(dtype).max - tensor_scaled = tensor.float() / scale - torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled) + tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype) + # TODO: uncomment this if it's actually needed because the clamp has a small performance penality' + # lp_amax = torch.finfo(dtype).max + # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled) qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format) layout_params = {